Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.
Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to such transactional networks being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is hard. This motivated us explore quantum machine learning algorithms that can probably be better for such hard problems. This paper proposes to improve phishing detection and performance of QML algorithms such as Quantum Support Vector Machine (QSVM) and Variational Quantum Classifier (VQC) by using a cascaded form of Quantum Random Access Coding (QRAC) encoding scheme. We present detailed ablation studies with other encoding strategies, and comparing with classical ML algorithms and quantum algorithms on simulators and real quantum hardware. For QSVM, we have tried two different implementations of QSVM - one using Kernel estimation using quantum circuits on universal quantum computers, and the other using annealers and for VQC, we benchmarked with different ansatzes with different entangling capacities. For both algorithms we tried different feature maps but consistently observe that QRAC-VQC gives 13% improvement and QRAC-QSVM gives 3% improvement over baselines. Overall we observe that QSVM gives least false negatives thus better at detecting phishing nodes even over classical state-of-art graph convolutional networks. Another important and interesting observation is that the performance of these QML algorithms does not drastically drop with currently available noisy hardware and is almost equal to simulator for a quantum device with high quantum volume or performance.
The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary classification of fraudulent financial activities. As of our methodology, first, we develop a comprehensive behavioural feature engineering framework to transform raw transactional data into a rich, descriptive feature set. Second, we implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset. The classical baseline models include Logistic Regression, Decision Tree, Random Forest, and XGBoost. These are compared against three hybrid classic quantum algorithms architectures: a Quantum Support Vector Machine (QSVM), a Variational Quantum Classifier (VQC), and a Hybrid Quantum Neural Network (HQNN). Furthermore, we propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment, featuring a classical-first, quantum-enhanced philosophy with robust fallback mechanisms. Our results demonstrate that classical tree-based models, particularly \textit{Random Forest}, significantly outperform the quantum counterparts in the current setup, achieving high accuracy (97.34%) and F-measure (86.95%). Among the quantum models, \textbf{QSVM} shows the most promise, delivering high precision (77.15%) and a low false-positive rate (1.36%), albeit with lower recall and significant computational overhead. This report provides a benchmark for a real-world financial application, highlights the current limitations of quantum machine learning in this domain, and outlines promising directions for future research.
Phishing remains a major cybersecurity threat, yet the application of quantum machine learning (QML) to phishing detection is still at an early stage. This study presents a systematic literature review aimed at providing a concise overview of existing QML-based approaches for phishing detection, identifying methodological trends, limitations, and future research directions. A PRISMA-guided review protocol was applied to peer-reviewed journal and conference articles published between 2021 and 2025, retrieved from major scientific databases. Eligible studies were analyzed in terms of QML models, feature encoding strategies, experimental settings, evaluation metrics, and study quality using an adapted Newcastle–Ottawa Scale. The results indicate that current research is limited in volume and largely focuses on hybrid quantum–classical models, particularly quantum support vector machines and variational quantum classifiers. Reported performance is highly dependent on encoding methods, circuit depth, and simulator-based experimentation, with few studies evaluating real quantum hardware. Common challenges include small datasets, lack of external validation, hardware noise, scalability constraints, and the absence of standardized benchmarks. Overall, the review suggests that QML for phishing detection remains exploratory and is not yet competitive with mature classical approaches, but it holds potential as an experimental research direction, provided that future studies address robustness, reproducibility, and practical deployment constraints.
The quick pace of digital financial transactions and the development of new strategies that fraudsters use makes the process of financial fraud detection a more than a complex task. The methods of machine learning have proved highly effective in detecting fraudulent behaviour especially in comparison to the traditional rule based systems. Simultaneously, the recent development of quantum computing has encouraged the consideration of quantum machine learning methods as the possible alternative or improvements to the classical models. The following paper contains a detailed comparative study of the traditional and quantum machine learning models of detecting financial fraud with references to a peer-reviewed literature and real financial data features. Classical algorithms such as ensemble and deep learning algorithms are discussed, as well as quantum algorithms such as quantum support vector machines and variational quantum classifiers. A single system architecture is presented and a comparison of the reported performance trends is carried out. It analyzes the persistence of classical models in the large-scale deployments and the emergence of hybrid classical-quantum frameworks as a promising research direction. The paper ends with a description of the major research gaps and practical challenges that should be overcome in order to implement quantum techniques in the real world financial systems.
The rapid expansion of the fintech sector has brought with it an increasing demand for robust and sophisticated fraud detection systems capable of managing large volumes of financial transactions. Conventional machine learning (ML) approaches, while effective, often encounter limitations in terms of computational efficiency and the ability to model complex, high-dimensional data structures. Recent advancements in quantum computing have given rise to a promising paradigm known as quantum machine learning (QML), which leverages quantum mechanical principles to solve problems that are computationally infeasible for classical computers. The integration of QML with data science has opened new avenues for enhancing fraud detection frameworks by improving the accuracy and speed of transaction pattern analysis, anomaly detection, and risk mitigation strategies within fintech ecosystems. This paper aims to explore the potential of quantum-enhanced data science methodologies to bolster fraud detection and prevention mechanisms, providing a comparative analysis of QML techniques against classical ML models in the context of their application to financial data analysis. Fraud detection in fintech relies heavily on data-driven models to identify suspicious activities and prevent financial crimes such as identity theft, money laundering, and fraudulent transactions. Traditional ML approaches, such as decision trees, support vector machines, and deep learning, have laid the foundation for these systems. However, these approaches often fall short when faced with the challenges posed by high-dimensional, noisy, and complex financial data. Quantum machine learning, by leveraging quantum bits or qubits, possesses the unique ability to represent and process data in an exponentially larger state space, allowing for more efficient pattern recognition and computationally intensive analysis. Quantum algorithms such as the Quantum Support Vector Machine (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum Neural Networks (QNNs) have been studied for their potential to outperform classical counterparts in specific problem domains, including fraud detection. This research delves into the theoretical foundations of quantum computing, outlining how quantum superposition, entanglement, and quantum interference can be harnessed to perform operations that exponentially accelerate data processing. Quantum algorithms are presented as capable of achieving faster data transformations and more nuanced pattern recognition through their ability to process all potential combinations of data simultaneously. The implementation of QML algorithms on quantum hardware, although still in its nascent stages, is beginning to demonstrate tangible benefits in terms of the speed and complexity of computations for fraud detection tasks. For example, quantum-enhanced anomaly detection can lead to the identification of rare, complex patterns that classical ML might overlook, contributing to a more proactive approach to fraud prevention. The paper also examines the integration of data science techniques with quantum-enhanced fraud detection, considering data preprocessing, feature engineering, and the application of quantum-enhanced statistical methods. Data preprocessing, a crucial step in building effective fraud detection models, involves the transformation and normalization of financial data to ensure that models can learn from relevant features without overfitting or underfitting. Quantum data structures offer the potential to represent data with a higher degree of complexity and interrelations, which is critical for capturing the multifaceted nature of financial transactions and detecting subtle signs of fraudulent activity. Quantum data encoding schemes such as Quantum Random Access Memory (QRAM) enable efficient storage and retrieval of data, providing a scalable solution for processing large datasets in real-time. A comprehensive analysis of case studies demonstrates the real-world applicability of quantum machine learning frameworks in fintech. The research highlights projects where quantum algorithms have been tested in controlled environments to detect anomalies in simulated transaction data, showcasing improvements in the identification of complex fraud scenarios over classical ML approaches. For instance, Quantum Support Vector Machines have been utilized to perform higher-dimensional classification tasks that are essential for distinguishing between legitimate and fraudulent transactions based on transaction history and user behavior. Furthermore, quantum algorithms that operate on hybrid systems, combining quantum and classical resources, are also explored to mitigate the limitations imposed by current quantum hardware, which is still constrained by issues such as noise and qubit coherence time. The paper also addresses key challenges and limitations associated with the integration of QML into practical fraud detection systems. Quantum hardware, although advancing rapidly, still faces significant challenges, including the need for error correction, qubit stability, and hardware scalability. Quantum computers with sufficient qubits and coherence time are necessary to implement complex algorithms for fraud detection effectively. Additionally, a practical approach to harnessing QML would require the development of quantum software frameworks and quantum programming languages that can operate in tandem with existing fintech systems and data infrastructure. Another area of focus is the synergy between quantum machine learning and classical machine learning models in creating hybrid systems that leverage the strengths of both methodologies. Quantum-enhanced feature extraction and dimensionality reduction can be combined with classical algorithms for final decision-making processes. This allows for a more comprehensive approach where quantum algorithms handle the computationally intensive parts of data analysis, while classical systems can be utilized for integrating real-time data and refining output for human interpretation. The paper discusses potential pathways for integrating these hybrid models, including considerations for API development, data interoperability, and the standardization of quantum-classical workflows. The discussion extends to the practical implications of implementing quantum-based fraud detection systems, particularly in terms of security and privacy. The use of quantum encryption and quantum key distribution can complement QML by ensuring that the data fed into fraud detection models is protected from external tampering. Quantum-resistant cryptography solutions are also explored, providing a comprehensive view of how quantum technologies could enhance the overall security posture of fintech ecosystems while promoting trust and compliance.